# to import necessary libraries
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report, accuracy_score

from sklearn.ensemble import RandomForestClassifier

#load dataset
df = pd.read_csv('c:\\Users\\HP\\Downloads\\DS-ML\Churn_Modelling.csv')
df.head()

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Churn
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0

# getting information about dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 14 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   RowNumber        10000 non-null  int64  
 1   CustomerId       10000 non-null  int64  
 2   Surname          10000 non-null  object 
 3   CreditScore      10000 non-null  int64  
 4   Geography        10000 non-null  object 
 5   Gender           10000 non-null  object 
 6   Age              10000 non-null  int64  
 7   Tenure           10000 non-null  int64  
 8   Balance          10000 non-null  float64
 9   NumOfProducts    10000 non-null  int64  
 10  HasCrCard        10000 non-null  int64  
 11  IsActiveMember   10000 non-null  int64  
 12  EstimatedSalary  10000 non-null  float64
 13  Churn            10000 non-null  int64  
dtypes: float64(2), int64(9), object(3)
memory usage: 1.1+ MB

# more info on rows and collumns
df.shape

(10000, 14)

#information about missing data
round((df.isnull().sum() / df.shape[0]) * 100, 2)

RowNumber          0.0
CustomerId         0.0
Surname            0.0
CreditScore        0.0
Geography          0.0
Gender             0.0
Age                0.0
Tenure             0.0
Balance            0.0
NumOfProducts      0.0
HasCrCard          0.0
IsActiveMember     0.0
EstimatedSalary    0.0
Churn              0.0
dtype: float64

# identify collumn data types
cat_col = [col for col in df.columns if df[col].dtype == 'object']
num_col = [col for col in df.columns if df[col].dtype != 'object']

print('Categorical columns:', cat_col)
print('Numerical columns:', num_col)

Categorical columns: ['Surname', 'Geography', 'Gender']
Numerical columns: ['RowNumber', 'CustomerId', 'CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Churn']

#count unique data for categorical data types
df[cat_col].nunique()

Surname      2932
Geography       3
Gender          2
dtype: int64

#statistical summary of dataset
df.describe()

	RowNumber	CustomerId	CreditScore	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Churn
count	10000.00000	1.000000e+04	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.00000	10000.000000	10000.000000	10000.000000
mean	5000.50000	1.569094e+07	650.528800	38.921800	5.012800	76485.889288	1.530200	0.70550	0.515100	100090.239881	0.203700
std	2886.89568	7.193619e+04	96.653299	10.487806	2.892174	62397.405202	0.581654	0.45584	0.499797	57510.492818	0.402769
min	1.00000	1.556570e+07	350.000000	18.000000	0.000000	0.000000	1.000000	0.00000	0.000000	11.580000	0.000000
25%	2500.75000	1.562853e+07	584.000000	32.000000	3.000000	0.000000	1.000000	0.00000	0.000000	51002.110000	0.000000
50%	5000.50000	1.569074e+07	652.000000	37.000000	5.000000	97198.540000	1.000000	1.00000	1.000000	100193.915000	0.000000
75%	7500.25000	1.575323e+07	718.000000	44.000000	7.000000	127644.240000	2.000000	1.00000	1.000000	149388.247500	0.000000
max	10000.00000	1.581569e+07	850.000000	92.000000	10.000000	250898.090000	4.000000	1.00000	1.000000	199992.480000	1.000000

#correlation analysis
df.corr(numeric_only=True)

	RowNumber	CustomerId	CreditScore	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Churn
RowNumber	1.000000	0.004202	0.005840	0.000783	-0.006495	-0.009067	0.007246	0.000599	0.012044	-0.005988	-0.016571
CustomerId	0.004202	1.000000	0.005308	0.009497	-0.014883	-0.012419	0.016972	-0.014025	0.001665	0.015271	-0.006248
CreditScore	0.005840	0.005308	1.000000	-0.003965	0.000842	0.006268	0.012238	-0.005458	0.025651	-0.001384	-0.027094
Age	0.000783	0.009497	-0.003965	1.000000	-0.009997	0.028308	-0.030680	-0.011721	0.085472	-0.007201	0.285323
Tenure	-0.006495	-0.014883	0.000842	-0.009997	1.000000	-0.012254	0.013444	0.022583	-0.028362	0.007784	-0.014001
Balance	-0.009067	-0.012419	0.006268	0.028308	-0.012254	1.000000	-0.304180	-0.014858	-0.010084	0.012797	0.118533
NumOfProducts	0.007246	0.016972	0.012238	-0.030680	0.013444	-0.304180	1.000000	0.003183	0.009612	0.014204	-0.047820
HasCrCard	0.000599	-0.014025	-0.005458	-0.011721	0.022583	-0.014858	0.003183	1.000000	-0.011866	-0.009933	-0.007138
IsActiveMember	0.012044	0.001665	0.025651	0.085472	-0.028362	-0.010084	0.009612	-0.011866	1.000000	-0.011421	-0.156128
EstimatedSalary	-0.005988	0.015271	-0.001384	-0.007201	0.007784	0.012797	0.014204	-0.009933	-0.011421	1.000000	0.012097
Churn	-0.016571	-0.006248	-0.027094	0.285323	-0.014001	0.118533	-0.047820	-0.007138	-0.156128	0.012097	1.000000

sns.countplot(x='Churn', data=df, hue='Churn', palette='Set1')
plt.title('Churn Modelling')
plt.show()

# generate profiling report 
from ydata_profiling import ProfileReport
df.profile_report()

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Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

100%|██████████| 14/14 [00:00<00:00, 36.68it/s]

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plt.boxplot(df['Age'], vert=False)
plt.ylabel('Variable')
plt.xlabel('Age')
plt.title('Box Plot')
plt.show()

X = df[['CustomerId','Surname','CreditScore', 'Geography','Gender','Age','Tenure', 'Balance','NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary']]
Y = df['Churn']

from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder

scaler = MinMaxScaler(feature_range=(0, 1))

x1 = X
num_cols = x1.select_dtypes(include=['int64', 'float64']).columns
x1[num_cols] = scaler.fit_transform(x1[num_cols])

# encoding categorical features
cat_cols = x1.select_dtypes(include=['object']).columns
le = LabelEncoder()
for col in cat_cols:
    x1[cat_cols] = le.fit_transform(x1[cat_cols])
x1.head()

C:\Users\HP\AppData\Local\Temp\ipykernel_8788\4292956829.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  x1[num_cols] = scaler.fit_transform(x1[num_cols])

	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary
0	0.275616	0.380416	0.538	0.0	0.0	0.324324	0.2	0.000000	0.000000	1.0	1.0	0.506735
1	0.326454	0.401569	0.516	1.0	0.0	0.310811	0.1	0.334031	0.000000	0.0	1.0	0.562709
2	0.214421	0.696008	0.304	0.0	0.0	0.324324	0.8	0.636357	0.666667	1.0	0.0	0.569654
3	0.542636	0.098601	0.698	0.0	0.0	0.283784	0.1	0.000000	0.333333	0.0	0.0	0.469120
4	0.688778	0.621631	1.000	1.0	0.0	0.337838	0.2	0.500246	0.000000	1.0	1.0	0.395400

# standardizing numerical features
scaler = StandardScaler()
X_standardized = scaler.fit_transform(x1)
print(X_standardized[:5])

[[-0.78321342 -0.46418322 -0.32622142 -0.90188624 -1.09598752  0.29351742
  -1.04175968 -1.22584767 -0.91158349  0.64609167  0.97024255  0.02188649]
 [-0.60653412 -0.3909112  -0.44003595  1.51506738 -1.09598752  0.19816383
  -1.38753759  0.11735002 -0.91158349 -1.54776799  0.97024255  0.21653375]
 [-0.99588476  0.62898807 -1.53679418 -0.90188624 -1.09598752  0.29351742
   1.03290776  1.33305335  2.52705662  0.64609167 -1.03067011  0.2406869 ]
 [ 0.14476652 -1.44035563  0.50152063 -0.90188624 -1.09598752  0.00745665
  -1.38753759 -1.22584767  0.80773656 -1.54776799 -1.03067011 -0.10891792]
 [ 0.65265871  0.37135419  2.06388377  1.51506738 -1.09598752  0.38887101
  -1.04175968  0.7857279  -0.91158349  0.64609167  0.97024255 -0.36527578]]

# preparing for train-test split
categorical_features_X = X.select_dtypes(include=['object']).columns.tolist()
numerical_features_X = X.select_dtypes(include=['int64', 'float64']).columns.tolist()

preprocessor = ColumnTransformer(
    transformers=[
        ("cat", OneHotEncoder(handle_unknown='ignore'), categorical_features_X),
        ("num", StandardScaler(), numerical_features_X)
    ]
)

# train-test split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=42)

# building random forest model
rf_model = Pipeline(steps=[
    ('preprocess', preprocessor),
    ('model', RandomForestClassifier(
        n_estimators=300,
        max_depth=None,
        random_state=42,
        class_weight='balanced'  # handle imbalanced classes
    ))
])
# Fit
rf_model.fit(X_train, Y_train)

Pipeline(steps=[('preprocess',
                 ColumnTransformer(transformers=[('cat',
                                                  OneHotEncoder(handle_unknown='ignore'),
                                                  []),
                                                 ('num', StandardScaler(),
                                                  ['CustomerId', 'Surname',
                                                   'CreditScore', 'Geography',
                                                   'Gender', 'Age', 'Tenure',
                                                   'Balance', 'NumOfProducts',
                                                   'HasCrCard',
                                                   'IsActiveMember',
                                                   'EstimatedSalary'])])),
                ('model',
                 RandomForestClassifier(class_weight='balanced',
                                        n_estimators=300, random_state=42))])

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# accuracy and classification report
rf_model.fit(X_train, Y_train)

rf_preds = rf_model.predict(X_test)

print("\n=== RANDOM FOREST RESULTS ===")
print("Accuracy:", accuracy_score(Y_test, rf_preds))
print(classification_report(Y_test, rf_preds))

=== RANDOM FOREST RESULTS ===
Accuracy: 0.8664
              precision    recall  f1-score   support

           0       0.87      0.98      0.92      2003
           1       0.81      0.42      0.56       497

    accuracy                           0.87      2500
   macro avg       0.84      0.70      0.74      2500
weighted avg       0.86      0.87      0.85      2500